Sorting Algorithm Benchmark

Academic project for Data Structures II. Benchmarks five sorting algorithms in C across multiple input sizes, then generates performance charts via Python/matplotlib.

Algorithms

Algorithm	Average Case	Worst Case
Bubble Sort	O(n²)	O(n²)
Selection Sort	O(n²)	O(n²)
Insertion Sort	O(n²)	O(n²)
Quick Sort	O(n log n)	O(n²)
Heap Sort	O(n log n)	O(n log n)

How It Works

1. C program (main.c + sorting.c) generates random integer arrays at sizes [100, 1000, 5000, 10000, 50000, 100000], runs each algorithm on a copy, and writes per-run metrics (swaps, comparisons, elapsed time) to binary files under ./executions/.
2. Python script (main.py) compiles and runs the C program, reads the binary data, and saves performance charts to ./graficos/.

Requirements

• GCC
• Python 3
• matplotlib (pip install matplotlib)

Usage

# Run benchmark + generate charts
python main.py

Or run only the C benchmark:

make
./Trabalho_Pratico_II

Output

Charts saved to ./graficos/:

• relatorio_completo.png — swaps, comparisons, and time in one figure
• trocas_comparacao.png — swaps per algorithm
• comparacoes_comparacao.png — comparisons per algorithm
• tempo_comparacao.png — execution time per algorithm

Project Structure

.
├── main.c          # Benchmark driver
├── sorting.c       # Algorithm implementations
├── sorting.h       # Function declarations
├── main.py         # Data reader + chart generator
├── makefile
├── requirements.txt
└── executions/     # Binary result files (generated at runtime)